Charles Perreault

The Pace of Cultural Evolution

Today, humans inhabit most of the world’s terrestrial habitats. This observation has been explained by the fact that we possess a secondary inheritance mechanism, culture, in addition to a genetic system. Because it is assumed that cultural evolution occurs faster than biological evolution, humans can adapt to new ecosystems more rapidly than other animals. This assumption, however, has never been tested empirically. Here, I compare rates of change in human technologies to rates of change in animal morphologies. I find that rates of cultural evolution are inversely correlated with the time interval over which they are measured, which is similar to what is known for biological rates. This correlation explains why the pace of cultural evolution appears faster when measured over recent time periods, where time intervals are often shorter. Controlling for the correlation between rates and time intervals, I show that (1) cultural evolution is faster than biological evolution; (2) this effect holds true even when the generation time of species is controlled for; and (3) culture allows us to evolve over short time scales, which are normally accessible only to short-lived species, while at the same time allowing for us to enjoy the benefits of having a long life history.

Dating the origin of language using phonemic diversity

Language is a key adaptation of our species, yet we do not know when it evolved. Here, we use data on language phonemic diversity to estimate a minimum date for the origin of language. We take advantage of the fact that phonemic diversity evolves slowly and use it as a clock to calculate how long the oldest African languages would have to have been around in order to accumulate the number of phonemes they possess today. We use a natural experiment, the colonization of Southeast Asia and Andaman Islands, to estimate the rate at which phonemic diversity increases through time. Using this rate, we estimate that present-day languages date back to the Middle Stone Age in Africa. Our analysis is consistent with the archaeological evidence suggesting that complex human behavior evolved during the Middle Stone Age in Africa, and does not support the view that language is a recent adaptation that has sparked the dispersal of humans out of Africa. While some of our assumptions require testing and our results rely at present on a single case-study, our analysis constitutes the first estimate of when language evolved that is directly based on linguistic data.

Measuring the Complexity of Lithic Technology

Assessments of the complexity of lithic technologies coming from different time periods, regions, or hominid species are recurrent features of the literature on Paleolithic archaeology. Yet the notion of lithic complexity is often defined intuitively and qualitatively, which can easily lead to circular arguments and makes difficult the comparison of assemblages across different regions and time periods. Here we propose, in the spirit of Oswalt’s techno-units, that the complexity of lithic technology can be quantified by counting the procedural units involved in tool manufacture. We define procedural units as mutually exclusive manufacturing steps that make a distinct contribution to the finished form of a technology. As a proof of concept, we use the procedural-unit approach to measure the complexity of 13 Paleolithic assemblages. While preliminary, these results provide a quantitative benchmark confirming that lithic technological complexity increased throughout the Paleolithic period. The method to measure lithic complexity outlined here will allow us to revisit several claims made about change in technological complexity during human evolution.

Behavioural variation in 172 small-scale societies indicates that social learning is the main mode of human adaptation.

The behavioural variation among human societies is vast and unmatched in the animal world. It is unclear whether this variation is due to variation in the ecological environment or to differences in cultural traditions. Underlying this debate is a more fundamental question: is the richness of humans’ behavioural repertoire due to non-cultural mechanisms, such as causal reasoning, inventiveness, reaction norms, trial-and-error learning and evoked culture, or is it due to the population-level dynamics of cultural transmission? Here, we measure the relative contribution of environment and cultural history in explaining the behavioural variation of 172 Native American tribes at the time of European contact. We find that the effect of cultural history is typically larger than that of environment. Behaviours also persist over millennia within cultural lineages. This indicates that human behaviour is not predominantly determined by single-generation adaptive responses, contra theories that emphasize non-cultural mechanisms as determinants of human behaviour. Rather, the main mode of human adaptation is social learning mechanisms that operate over multiple generations.

A note on reconstructing animal social networks from independent small-group observations

Animal social networks are often built by aggregating a series of independent observations of two or more members of a group interacting or in association. Every time an observation is made, edges are drawn between each pair of individuals involved. I examined the effect of edge sample size on the reconstruction of social networks. I created different artificial networks and sampled edges from each. I estimated and compared the number of nodes, number of components, path length, clustering coefficient, network density, mean degree, betweenness centrality and degree probability distribution of the reconstructed networks to the true value of the network. I describe how the accuracy of these measures changes as the fraction of sampled edges increases. I show that edge sample size affects network measures in different ways and that when an incomplete sample is analysed, network properties can be considerably misrepresented. I also show that, because animal networks are typically small, simple curve fitting to the degree distribution P(k) should be done with caution, because different curve models can show significant fit for the same data. Overall, the results indicate that strong claims about animal social networks should not be made unless considerable effort has been made to collect an exhaustive number of association/interaction data points. If observations of associations/interactions are accumulated over a long period, the effect of increasing edge sample size could be mistaken for temporal change in social network and could also muddy the comparison of network structure between populations and between species.

EDITORIAL — CULTURAL EVOLUTION IN SPATIALLY STRUCTURED POPULATIONS: A REVIEW OF ALTERNATIVE MODELING FRAMEWORKS

We consider the dynamics of cultural evolution in spatially-structured populations. Most spatially explicit modeling approaches can be broadly divided into two classes: micro- and macro-level models. Macro-level models study cultural evolution at the population level and describe the average behavior of the considered system. Conversely, micro-level models focus on the constituent units of the system, and study the evolutionary dynamics that emerge out of the interaction between these units. In this paper, we give an overview of the general properties of micro- and macro-level models using the examples of agent-based simulations and of continuum models based in diffusion theory; we highlight how both frameworks account for spatially-dependent processes. We argue that both micro- and macro-level models are well-suited to describe the process of cultural evolution in spatial settings and stress that micro- and macro-level models should not be considered as competing alternatives, but rather as complementary tools that can provide different insights into cultural evolutionary dynamics. Although adding spatial components to any model increases its complexity, we argue (based on the findings presented by contributors to this Special Issue of Advances in Complex Systems), that the incorporation of space into the evolutionary framework is a necessary step towards a more complete understanding of the process of cultural evolution.

Divide and conquer: intermediate levels of population fragmentation maximize cultural accumulation

Identifying the determinants of cumulative cultural evolution is a key issue in the interdisciplinary field of cultural evolution. A widely held view is that large and well-connected social networks facilitate cumulative cultural evolution because they promote the spread of useful cultural traits and prevent the loss of cultural knowledge through factors such as drift. This view stems from models that focus on the transmission of cultural information, without considering how new cultural traits actually arise. In this paper, we review the literature from various fields that suggest that, under some circumstances, increased connectedness can decrease cultural diversity and reduce innovation rates. Incorporating this idea into an agent-based model, we explore the effect of population fragmentation on cumulative culture and show that, for a given population size, there exists an intermediate level of population fragmentation that maximizes the rate of cumulative cultural evolution. This result is explained by the fact that fully connected, non-fragmented populations are able to maintain complex cultural traits but produce insufficient variation and so lack the cultural diversity required to produce highly complex cultural traits. Conversely, highly fragmented populations produce a variety of cultural traits but cannot maintain complex ones. In populations with intermediate levels of fragmentation, cultural loss and cultural diversity are balanced in a way that maximizes cultural complexity. Our results suggest that population structure needs to be taken into account when investigating the relationship between demography and cumulative culture. This article is part of the theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution’.

The impact of site sample size on the reconstruction of culture histories

I examine how our capacity to produce accurate culture-historical reconstructions changes as more archaeological sites are discovered, dated, and added to a data set. More precisely, I describe, using simulated data sets, how increases in the number of known sites impact the accuracy and precision of our estimations of (1) the earliest and (2) latest date of a cultural tradition, (3) the date and (4) magnitude of its peak popularity, as well as (5) its rate of spread and (6) disappearance in a population. I show that the accuracy and precision of inferences about these six historical processes are not affected in the same fashion by changes in the number of known sites. I also consider the impact of two simple taphonomic site destruction scenarios on the results. Overall, the results presented in this paper indicate that unless we are in possession of near-total samples of sites, and can be certain that there are no taphonomic biases in the universe of sites to be sampled, we will make inferences of varying precision and accuracy depending on the aspect of a cultural trait’s history in question.

Cultural history, not ecological environment, is the main determinant of human behaviour

Towner et al. [[1][1]] question the methods and the theoretical framework of our study of behavioural variation among Native American tribes of Western North America [[2][2]]. Here we show that their concerns are unfounded and that our results are robust. We also clarify the theoretical issues that

Explaining group-level traits requires distinguishing process from product.

Smaldino is right to argue that we need a richer theory of group-level traits. He is wrong, however, in limiting group-level traits to units of cultural selection, which require explanations based on group selection. Traits are best understood when explanations focus on both process (i.e., selection) and product (i.e., adaptation). This approach can distinguish group-level traits that arise through within-group processes from those that arise through between-group processes.